Data & AI1:1 mentoring

Data Science with Python

Build the full data science toolkit: NumPy, pandas, statistics, exploratory analysis and your first models with scikit-learn.

Level:: Beginner to intermediate
Format:: 1:1 online mentoring

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Free, no-obligation chat · Affordable plans that fit your budget · Start this week

1:1 personal attentionOne mentor, one student, at your pace.
Flexible timingsSessions that fit your week and time zone.
Affordable plansPlans that fit your budget.
Practise between sessionsShort problems that build confidence.

About this course

Learn Data Science with Python online

Data science combines programming, statistics and domain knowledge to find patterns and make predictions from data. Beginners often feel lost between all three: which library does what, how much statistics is enough, and how to tell whether a model is genuinely good or just memorising the data.

This course follows a standard data science curriculum: Python for data, NumPy arrays, pandas, probability and descriptive statistics, hypothesis testing, exploratory data analysis and visualisation, feature engineering, and an introduction to modelling with scikit-learn, including train-test splits and evaluation. Each module lists its topics and what you will be able to do.

Learning is through affordable 1:1 mentoring, where a mentor explains each idea with small datasets and reviews your notebooks with you. Message us on WhatsApp or email, or fill in the short form, for a free, no-obligation chat. We will suggest a plan that fits your budget and flexible timings, and you can start this week.

Who this course is for

Beginners who want to start a data science career
Computer science, maths and statistics students
Data analysts who want to move into modelling
Engineers and professionals changing careers into data
Learners who plan to continue to machine learning

Outcomes

What you'll learn

Use NumPy arrays and vectorised operations instead of slow loops
Load, clean and transform data with pandas
Describe data with the right statistics and visualisations
Apply probability ideas, sampling and hypothesis tests correctly
Run a structured exploratory data analysis and report the findings
Prepare features: encoding, scaling and handling missing values
Train and evaluate a first regression and classification model with scikit-learn
Explain overfitting and use train-test splits and cross-validation

Skills you'll gain

Python
NumPy
pandas
Statistics
Probability
Hypothesis testing
Exploratory analysis
Data visualisation
Feature engineering
scikit-learn
Model evaluation
Jupyter notebooks

Detailed syllabus

Data Science with Python syllabus

Every module lists its topics and what you will be able to do. Your mentor plans sessions around them and your own goals.

Module 1
Python and tools for data science
The environment and Python skills every data scientist uses daily.
- The data science workflow
- Installing Python, Jupyter and virtual environments
- Python data structures
- Functions and list comprehensions
- Working with files
- Reading documentation
- Version control with Git basics
By the end of this module you can
- Set up a reproducible data science environment
- Write clean Python functions for data tasks
Covered in 1:1 mentoring sessions
Module 2
NumPy and numerical computing
Fast array computing, the base of the whole Python data ecosystem.
- ndarray, shape, dtype and axes
- Creating arrays
- Indexing and slicing
- Boolean indexing
- Vectorised operations and broadcasting
- Aggregations along axes
- Random numbers and seeds
- Basic linear algebra: dot, transpose, inverse
By the end of this module you can
- Replace Python loops with vectorised NumPy code
- Explain broadcasting with a small example
Covered in 1:1 mentoring sessions
Module 3
Data wrangling with pandas
Loading, cleaning and reshaping real datasets.
- Series and DataFrame
- Reading CSV and other files
- Selecting with loc, iloc and masks
- Missing values and duplicates
- Type conversion
- groupby and aggregation
- merge and concat
- Dates and times
By the end of this module you can
- Clean a real dataset end to end with pandas
- Summarise data by groups and combine several tables
Covered in 1:1 mentoring sessions
Module 4
Descriptive statistics and visualisation
Understanding data by summarising and plotting it.
- Mean, median, mode
- Variance and standard deviation
- Quartiles and the IQR
- Skewness and outliers
- Histograms, box plots and scatter plots
- Correlation and covariance
- Plotting with matplotlib and seaborn
- Misleading charts
By the end of this module you can
- Choose summary statistics that suit the shape of the data
- Create charts that show distributions and relationships clearly
Covered in 1:1 mentoring sessions
Module 5
Probability foundations
The language of uncertainty behind every model.
- Sample spaces and events
- Conditional probability and independence
- Bayes' theorem
- Random variables
- Binomial, Poisson and normal distributions
- Expected value and variance
- Law of large numbers and the central limit theorem
- Simulating probability with NumPy
By the end of this module you can
- Solve basic probability problems and simulate them in code
- Explain the central limit theorem and why it matters
Covered in 1:1 mentoring sessions
Module 6
Statistical inference
Drawing conclusions about a population from a sample.
- Populations, samples and sampling bias
- Confidence intervals
- Null and alternative hypotheses
- p-values and their meaning
- t-tests and chi-square tests
- Type I and Type II errors
- A/B testing
- Correlation versus causation
By the end of this module you can
- Run and interpret a t-test or chi-square test
- Explain a p-value without misusing it
Covered in 1:1 mentoring sessions
Module 7
Exploratory data analysis
A repeatable way to learn what a dataset contains before modelling.
- Asking questions first
- Shape, types and missing data
- Univariate analysis
- Bivariate and multivariate analysis
- Group comparisons
- Outlier handling
- Finding data leakage and quality issues
- Writing the findings
By the end of this module you can
- Carry out a full EDA and record the key insights
- Spot data quality problems before they ruin a model
Covered in 1:1 mentoring sessions
Module 8
Feature engineering and preparation
Turning raw columns into inputs a model can use.
- Numerical and categorical features
- Imputing missing values
- One-hot and ordinal encoding
- Scaling: standardisation and min-max
- Creating new features from dates and text
- Handling skewed data with transforms
- Feature selection basics
By the end of this module you can
- Prepare a dataset for modelling without leaking test information
- Pick an encoding and scaling method and say why
Covered in 1:1 mentoring sessions
Module 9
Introduction to modelling with scikit-learn
Training your first predictive models and using the estimator API.
- Supervised and unsupervised learning
- The fit, predict and score pattern
- Linear regression
- Logistic regression
- k-nearest neighbours
- Decision trees
- Train-test split
- Pipelines at a glance
By the end of this module you can
- Train a regression model and a classification model with scikit-learn
- Split data correctly and make predictions on unseen rows
Covered in 1:1 mentoring sessions
Module 10
Evaluating models
Measuring whether a model is genuinely good.
- Overfitting and underfitting
- Bias and variance
- Cross-validation
- Regression metrics: MAE, RMSE, R-squared
- Classification metrics: accuracy, precision, recall, F1
- Confusion matrix and ROC curve
- Baseline models
- Hyperparameter tuning with grid search
By the end of this module you can
- Choose metrics that match the problem, especially with imbalanced classes
- Use cross-validation to compare models fairly
Covered in 1:1 mentoring sessions
Module 11
Capstone project
Taking a problem from raw data to a communicated result.
- Choosing a question and a dataset
- Cleaning and EDA
- Feature preparation
- Training and comparing models
- Interpreting results
- Writing a report
- Presenting findings
- Publishing the project on GitHub
By the end of this module you can
- Deliver a complete data science project in a notebook
- Explain your approach and limits to a non-technical audience
Covered in 1:1 mentoring sessions

Ways to learn

How you can learn Data Science with Python

Data Science with Python mentor, 1:1 online

A mentor who knows this course plans sessions around your goal, explains each idea with fresh examples and checks your understanding. Personal attention, flexible timings in your time zone and affordable plans that fit your budget.

Talk to our team

How it works

Your learning journey

Four simple steps to start Data Science with Python with a mentor. A real person replies to you; there is no booking system.

Step 1: Talk to us
Message us on WhatsApp or email, or fill in the short form. The first chat is free, with no obligation.
Step 2: Get your plan
We listen to your goal, level and deadlines, then suggest a plan that fits your budget and timings.
Step 3: Learn with your mentor
Online 1:1 sessions with a mentor who knows your course, explains clearly and checks your understanding.
Step 4: Practise & track progress
Keep going between sessions with practice, and watch your confidence grow week by week.

Learn by doing

Projects and practice

House price prediction
Clean a housing dataset, explore it, engineer features and compare linear regression with a decision tree using cross-validation.
Customer churn classifier
Predict which customers are likely to leave, handle imbalanced classes and explain the result with precision and recall.
A/B test analysis
Analyse the results of a website experiment with a hypothesis test and write a clear recommendation.
Exploratory report on public data
Pick an open dataset, ask questions, test them with statistics and charts, and publish the notebook on GitHub.

Free practice on MentorNxt

Your progress

What you'll be able to do

Milestones from the start, middle and end of the syllabus.

Set up a reproducible data science environment
Early on
Run and interpret a t-test or chi-square test
Halfway
Deliver a complete data science project in a notebook
By the end

FAQs

Data Science with Python: frequently asked questions

Is there a self-study option?

Not for data science itself. Our free self-study lessons exist only for Java and Python programming, so this course is taught through 1:1 mentoring. You can still use our free Python lessons to build your programming basics.

Do I need to know Python or maths first?

Basic Python and school-level maths are enough to start. Your mentor will revisit any Python, probability or algebra you need as it comes up.

What is the difference between data science and machine learning?

Data science covers the whole process: collecting, cleaning, exploring and modelling data and communicating results. Machine learning is the modelling part. This course introduces it, and our machine learning course goes deeper.

Which libraries will I use?

NumPy, pandas, matplotlib, seaborn, SciPy or statsmodels for statistics, and scikit-learn for models, all inside Jupyter notebooks.

Can a mentor help with my university data science module?

Yes. Share your outline and upcoming topics, and your mentor will explain the concepts with fresh examples. Mentors teach and explain; they never complete graded work for students.

How much does mentoring cost?

1:1 mentoring is personal online tutoring and is affordable, with plans that fit your budget. Tell us your goal and how often you would like sessions, and our team will suggest an option that works for you in a free, no-obligation chat.

How do I get started?

Message us on WhatsApp or email, or fill in the short form on this page. Our team replies, talks through your goal and level, and arranges a mentor at flexible timings in your time zone. You can start this week.

Free, no-obligation chat

Talk to our team about Data Science with Python

Tell us what you need. Our support team replies within one working day, by email or WhatsApp, whichever you prefer, and arranges your mentor.

Prefer to talk first?

Message the support team directly. We usually reply the same day.

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Our mentors teach and explain. They never complete graded work, tests or assessments for students.

Keep going

Syllabus references

Last reviewed9 November 2026

Ready to start Data Science with Python?

Message us on WhatsApp or email, or fill in the short form. Our team replies with a plan that fits your goal and budget, and you can start this week.

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Learn Data Science with Python online

Who this course is for

What you'll learn

Skills you'll gain

Data Science with Python syllabus

Python and tools for data science

NumPy and numerical computing

Data wrangling with pandas

Descriptive statistics and visualisation

Probability foundations

Statistical inference

Exploratory data analysis

Feature engineering and preparation

Introduction to modelling with scikit-learn

Evaluating models

Capstone project

How you can learn Data Science with Python

Data Science with Python mentor, 1:1 online

Your learning journey

Step 1: Talk to us

Step 2: Get your plan

Step 3: Learn with your mentor

Step 4: Practise & track progress

Projects and practice

House price prediction

Customer churn classifier

A/B test analysis

Exploratory report on public data

Free practice on MentorNxt

What you'll be able to do

Data Science with Python: frequently asked questions

Talk to our team about Data Science with Python

Related courses

Data Analysis with Python

Machine Learning with Python

SQL and Databases

Python Programming

Deep Learning

Artificial Intelligence and Generative AI

Syllabus references

Ready to start Data Science with Python?

Learn Data Science with Python online

Who this course is for

What you'll learn

Skills you'll gain

Data Science with Python syllabus

Python and tools for data science

NumPy and numerical computing

Data wrangling with pandas

Descriptive statistics and visualisation

Probability foundations

Statistical inference

Exploratory data analysis

Feature engineering and preparation

Introduction to modelling with scikit-learn

Evaluating models

Capstone project

How you can learn Data Science with Python

Data Science with Python mentor, 1:1 online

Your learning journey

Step 1: Talk to us

Step 2: Get your plan

Step 3: Learn with your mentor

Step 4: Practise & track progress

Projects and practice

House price prediction

Customer churn classifier

A/B test analysis

Exploratory report on public data

Free practice on MentorNxt

What you'll be able to do

Data Science with Python: frequently asked questions

Talk to our team about Data Science with Python

Related courses

Data Analysis with Python

Machine Learning with Python

SQL and Databases

Python Programming

Deep Learning

Artificial Intelligence and Generative AI